Efforts to include behavioral measures in large-scale studies as envisioned by precision medicine are hampered by the time and expertise required. Paper-and-pencil tests currently dominating clinical assessment and neuropsychological testing are plainly unfeasible. The NIH Toolbox contains many computerized tests and clinical assessment tools varying in feasibility. Unique in the Toolbox is the Penn Computerized Neurocognitive Battery (CNB), which contains 14 tests that take one hour to administer. CNB has been validated with functional neuroimaging and in multiple normative and clinical populations across the lifespan worldwide, and is freely available for research. Clinical assessment tools are usually devoted to specific disorders, and scales vary in their concentration on symptoms that are disorder specific. We have developed a broad assessment tool (GOASSESS), which currently takes about one hour to administer. These instruments were constructed, optimized and validated with classical psychometric test theory (CTT), and are efficient as CTT allows. However, genomic studies require even more time-efficient tools that can be applied massively. Novel approaches, based on item response theory (IRT) can vastly enhance efficiency of testing and clinical assessment. IRT shifts the emphasis from the test to the items composing it by estimating item parameters such as ?difficulty? and ?discrimination? within ranges of general trait levels. IRT helps shorten the length of administration without compromising data quality, and for many domains leads to computer adaptive testing (CAT) that further optimizes tests to individual abilities. We propose to develop and validate adaptive versions of the CNB and GOASSESS, resulting in a neurocognitive and clinical screener that, using machine learning tools, will be continually optimized, becoming shorter and more precise as it is deployed. The tool will be in the Toolbox available in the public domain. We have item-level information to perform IRT analyses on existing data and use this information to develop CAT implementations and generate item pools for adaptive testing. Our Specific Aims are: 1. Use available itemwise data on the Penn CNB and the GOASSESS and add new tests and items to generate item pools for extending scope while abbreviating tests using IRT-CAT and other methods. The current item pool will be augmented to allow large selection of items during CAT administration and add clinical items to GOASSESS. New items will be calibrated through crowdsourcing. 2. Produce a modular CAT version of a neurocognitive and clinical assessment battery that covers major RDoC domains and a full range of psychiatric symptoms. We have implemented this procedure on some CNB tests and clinical scales and will apply similar procedures to remaining and new tests as appropriate. 3. Validate the CAT version in 100 individuals with psychosis spectrum disorders (PS), 100 with depression/anxiety disorders (DA), and 100 healthy controls (HC). We will use this dataset to implement and test data mining algorithms that optimize prediction of specific outcomes. All tests, algorithms and normative data will be in the toolbox.